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Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web environments to access data, answer queries, or infer new knowledge. In particular, ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics. Big data are being widely stored and exchanged in JavaScript Object Notation (JSON) format, in particular by Web applications. However, JSON data collections lack explicit semantics as they are in general schema-less, which does not allow to efficiently leverage the benefits of big data. Furthermore, several applications require bookkeeping of the entire history of big data changes, for which no support is provided by mainstream Big Data management systems, including Not only SQL (NoSQL) database systems. In this paper, we propose an approach, named τJOWL (temporal OWL 2 from temporal JSON), which allows users (i) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (ii) to manage its incremental maintenance accommodating the evolution of these data, in a temporal and multi-schema environment.


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τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data

Show Author's information Zouhaier Brahmia( )Fabio GrandiRafik Bouaziz
Faculty of Economics and Management, University of Sfax, Sfax 3029, Tunisia
Department of Computer Science and Engineering, University of Bologna, Bologna 40136, Italy

Abstract

Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web environments to access data, answer queries, or infer new knowledge. In particular, ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics. Big data are being widely stored and exchanged in JavaScript Object Notation (JSON) format, in particular by Web applications. However, JSON data collections lack explicit semantics as they are in general schema-less, which does not allow to efficiently leverage the benefits of big data. Furthermore, several applications require bookkeeping of the entire history of big data changes, for which no support is provided by mainstream Big Data management systems, including Not only SQL (NoSQL) database systems. In this paper, we propose an approach, named τJOWL (temporal OWL 2 from temporal JSON), which allows users (i) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (ii) to manage its incremental maintenance accommodating the evolution of these data, in a temporal and multi-schema environment.

Keywords:

big data, JavaScript Object Notation (JSON), JSON schema, temporal JSON, ontology, temporal ontology, τJSchema, τOWL
Received: 27 August 2021 Revised: 27 October 2021 Accepted: 01 November 2021 Published: 18 July 2022 Issue date: December 2022
References(67)
[1]
N. Guarino, Formal Ontology in Information Systems. Amsterdam, The Netherlands: IOS Press, 1998.
[2]
W3C, OWL 2 web ontology language primer (second edition), W3C recommendation 11 December 2012, , 2021.
[3]
P. F. Patel-Schneider and I. Horrocks, A comparison of two modelling paradigms in the Semantic Web, J. Web Semant., vol. 5, no. 4, pp. 240–250, 2007.
[4]
O. Etzioni, K. Golden, and D. S. Weld, Sound and efficient closed-world reasoning for planning, Artif. Intell., vol. 89, no. 1&2, pp. 113–148, 1997.
DOI
[5]
I. Seylan, E. Franconi, and J. De Bruijn, Effective query rewriting with ontologies over DBoxes, in Proc. 21st Int. Joint Conf. on Artificial Intelligence, Pasadena, CA, USA, 2009, pp. 923–929.
[6]
T. R. Rao, P. Mitra, R. Bhatt, and A. Goswami, The big data system, components, tools, and technologies: A survey, Knowl. Inf. Syst., vol. 60, no. 3, pp. 1165–1245, 2019.
[7]
A. Davoudian and M. C. Liu, Big data systems: A software engineering perspective, ACM Comput. Surv., vol. 53, no. 5, p. 110, 2020.
[8]
IETF, The JavaScript Object Notation (JSON) data interchange format, , 2021.
[9]
S. Banerjee, R. Shaw, A. Sarkar, and N. C. Debnath, Towards logical level design of big data, in Proc. of 2015 IEEE 13th Int. Conf. on Industrial Informatics, Cambridge, UK, 2015, pp. 1665–1671.
[10]
A. Hoppe, C. Nicolle, and A. Roxin, Automatic ontology-based user profile learning from heterogeneous web resources in a big data context, Proc. VLDB Endow., vol. 6, no. 12, pp. 1428–1433, 2013.
[11]
A. Soylu, M. Giese, E. Jimenez-Ruiz, E. Kharlamov, D. Zheleznyakov, and I. Horrocks, OptiqueVQS: Towards an ontology-based visual query system for big data, in Proc. 5th Int. Conf. on Management of Emergent Digital EcoSystems, Neumünster Abbey, Luxembourg, 2013, pp. 119–126.
[12]
C. Jayapandian, C. H. Chen, A. Dabir, S. Lhatoo, G. Q. Zhang, and S. S. Sahoo, Domain ontology as conceptual model for big data management: Application in biomedical informatics, in Proc. of the 33rd Int. Conf. on Conceptual Modeling, Atlanta, GA, USA, 2014, pp. 144–157.
[13]
T. Shah, F. Rabhi, and P. Ray, Investigating an ontology-based approach for Big Data analysis of inter-dependent medical and oral health conditions, Cluster Comput., vol. 18, no. 1, pp. 351–367, 2015.
[14]
J. P. C. Verhoosel and J. Spek, Applying ontologies in the dairy farming domain for big data analysis, in Proc. 3rd Stream Reasoning (SR 2016) and the 1st Semantic Web Technologies for the Internet of Things (SWIT 2016) Workshops Co-located with 15thInt. Semantic Web Conf. (ISWC 2016), Kobe, Japan, 2016, pp. 91–100.
[15]
A. R. Kim, H. A. Park, and T. M. Song, Development and evaluation of an obesity ontology for social big data analysis, Healthc. Inform. Res., vol. 23, no. 3, pp. 159–168, 2017.
[16]
H. Abbes and F. Gargouri, MongoDB-based modular ontology building for big data integration, J. Data Semant., vol. 7, no. 1, pp. 1–27, 2018.
[17]
L. S. Globa, R. L. Novogrudska, and A. V. Koval, Ontology model of telecom operator big data, in Proc. of 2018 IEEE Int. Black Sea Conf. on Communications and Networking, Batumi, GA, USA, 2018, pp. 1–5.
[18]
P. Wongthongtham and B. A. Salih, Ontology-based approach for identifying the credibility domain in social Big Data, J. Organ. Comput. Electron. Commer, vol. 28, no. 4, pp. 354–377, 2018.
[19]
S. Nadal, O. Romero, A. Abelló, P. Vassiliadis, and S. Vansummeren, An integration-oriented ontology to govern evolution in Big Data ecosystems, Inform. Syst., vol. 79, pp. 3–19, 2019.
[20]
P. S. Rani, R. M. Suresh, and R. Sethukarasi, Multi-level semantic annotation and unified data integration using semantic web ontology in big data processing, Cluster Comput., vol. 22, no. 5, pp. 10401–10413, 2019.
[21]
D. Djebouri and N. Keskes, Exploitation of ontological approaches in Big Data: A State of the Art, in Proc. 10th Int. Conf. on Information Systems and Technologies, Lecce, Italy, 2020, p. 45.
[22]
M. Y. Aghdam, S. R. K. Tabbakh, S. J. M. Chabok, and M. Kheyrabadi, Ontology generation for flight safety messages in air traffic management, J. Big Data, vol. 8, no. 1, p. 61, 2021.
[23]
S. Mhammedi, H. El Massari, and N. Gherabi, Cb2Onto: OWL ontology learning approach from couchbase, in Intelligent Systems in Big Data, Semantic Web and Machine Learning, N. Gherabi and J. Kacprzyk, eds. Cham, Germany: Springer, 2021, pp. 95–110.
DOI
[24]
I. Mountasser, B. Ouhbi, F. Hdioud, and B. Frikh, Semantic-based Big Data integration framework using scalable distributed ontology matching strategy, Distrib. Parallel Dat., vol. 39, no. 4, pp. 891–937, 2021.
[25]
F. Pezoa, J. L. Reutter, F. Suarez, M. Ugarte, and D. Vrgoc, Foundations of JSON Schema, in Proc. 25th Int. Conf. on World Wide Web, Montreal, Canada, 2016, pp. 263–273.
[26]
IETF, JSON Schema: A media type for describing JSON documents, , 2021.
[27]
L. Attouche, M. A. Baazizi, D. Colazzo, F. Falleni, G. Ghelli, C. Landi, C. Sartiani, and S. Scherzinger, A tool for JSON schema witness generation, in Proc. 24th Int. Conf. on Extending Database Technology, Nicosia, Cyprus, 2021, pp. 694–697.
[28]
JSON Schema, Implementations of JSON schema. Schema generators from data, , 2021.
[29]
Json-Schema-Inferrer, Java library for inferring JSON schema from sample JSONs, , 2021.
[30]
Schema Guru, , 2021.
[31]
Clojure JSON schema validator & generator, , 2021.
[32]
W3C, RDF/XML syntax specification (revised), W3C recommendation 10 February 2004, , 2021.
[33]
W3C, OWL 2 web ontology language document overview (second edition), W3C recommendation 11 December 2012, , 2021.
[34]
S. Brahmia, Z. Brahmia, F. Grandi, and R. Bouaziz, τJSchema: A framework for managing temporal JSON-Based NoSQL databases, in Proc. of the 27th Int. Conf. on Database and Expert Systems Applications, Porto, Portugal, 2016, pp. 167–181.
[35]
S. Brahmia, Z. Brahmia, F. Grandi, and R. Bouaziz, A disciplined approach to temporal evolution and versioning support in JSON data stores, in Emerging Technologies and Applications in Data Processing and Management, Z. M. Ma and L. Yan, eds. Hershey, PA, USA: IGI Global, 2019, pp. 114–133.
DOI
[36]
A. Zekri, Z. Brahmia, F. Grandi, and R. Bouaziz, τOWL: A framework for managing temporal semantic web documents, in Proc. of the 8th Int. Conf. on Advances in Semantic Processing, Rome, Italy, 2014, pp. 33–41.
[37]
A. Zekri, Z. Brahmia, F. Grandi, and R. Bouaziz, τOWL: A systematic approach to temporal versioning of semantic web ontologies, J. Data Semant., vol. 5, no. 3, pp. 141–163, 2016.
[38]
M. J. O’Connor and A. K. Das, A lightweight model for representing and reasoning with temporal information in biomedical ontologies, in Proc. 3rdInt. Conf. on Health Informatics, Valencia, Spain, 2010, pp. 90–97.
[39]
V. Milea, F. Frasincar, and U. Kaymak, tOWL: A temporal web ontology language, IEEE Trans. Syst. Man Cybern. B Cybern., vol. 42, no. 1, pp. 268–281, 2012.
[40]
E. Anagnostopoulos, S. Batsakis, and E. G. M. Petrakis, CHRONOS: A reasoning engine for qualitative temporal information in OWL, in Proc. of the 17th Int. Conf. in Knowledge Based and Intelligent Information and Engineering Systems, Kitakyushu, Japan, 2013, pp. 70–77.
[41]
S. Batsakis, E. G. M. Petrakis, I. Tachmazidis, and G. Antoniou, Temporal representation and reasoning in OWL 2, Semant. Web, vol. 8, no. 6, pp. 981–1000, 2017.
[42]
F. Ghorbel, F. Hamdi, E. Métais, N. Ellouze, and F. Gargouri, Ontology-based representation and reasoning about precise and imprecise temporal data: A fuzzy-based view, Data Knowl. Eng., vol. 124, p. 101719, 2019.
[43]
Z. Brahmia, S. Brahmia, F. Grandi, and R. Bouaziz, Implicit JSON schema versioning driven by big data evolution in the τJSchema framework, in Proc. of Int. Conf. on Big Data and Networks Technologies, Leuven, Belgium, 2019, pp. 23–35.
[44]
Z. Brahmia, S. Brahmia, F. Grandi, and R. Bouaziz, Implicit JSON schema versioning triggered by temporal updates to JSON-based Big Data in the τJSchema framework, in Proc. 5th Int. Conf. on Big Data and Internet of Things, Rabat, Morocco, .
[45]
Z. Brahmia, F. Grandi, A. Zekri, and R. Bouaziz, Ontology versioning driven by instance evolution in the τOWL framework, J. Inf. Knowl. Manag., .
[46]
Y. Han, H. Kim, J. Song, and T. M. Song, Ontology development of school bullying for social big data collection and analysis, J. Korea Contents Assoc., vol. 19, no. 6, pp. 10–23, 2019.
[47]
M. Wischenbart, S. Mitsch, E. Kapsammer, A. Kusel, B. Pröll, W. Retschitzegger, W. Schwinger, J. Schönböck, M. Wimmer, and S. Lechner, User profile integration made easy: Model-driven extraction and transformation of social network schemas, in Proc. 21st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 939–948.
[48]
M. Wischenbart, S. Mitsch, E. Kapsammer, A. Kusel, S. Lechner, B. Pröll, W. Retschitzegger, J. Schönböck, W. Schwinger, and M. Wimmer, Automatic data transformation-breaching the walled gardens of social network platforms, in Proc. of the 9th Asia-Pacific Conf. on Conceptual Modelling, Adelaide, Australia, 2013, pp. 89–98.
[49]
H. Abbes, S. Boukettaya, and F. Gargouri, Learning ontology from Big Data through MongoDB database, in Proc. of the 2015 IEEE/ACS 12th Int. Conf. of Computer Systems and Applications, Marrakech, Morocco, 2015, pp. 1–7.
[50]
Y. G. Yao, R. P. Wu, and H. Liu, JTOWL: A JSON to OWL Converto, in Proc. 5th Int. Workshop on Web-scale Knowledge Representation Retrieval & Reasoning, Shanghai, China, 2014, pp. 13–14.
DOI
[51]
G. B. Moreira, V. M. Calegario, J. C. Duarte, and A. F. P. dos Santos, Extending the VERIS framework to an incident handling ontology, in Proc. of 2018 IEEE/WIC/ACM Int. Conf. on Web Intelligence, Santiago, Chile, 2018, pp. 440–445.
[52]
H. Cheong, Translating JSON Schema logics into OWL axioms for unified data validation on a digital manufacturing platform, Procedia Manuf., vol. 28, pp. 183–188, 2019.
[53]
M. Ganzha, M. Paprzycki, W. Pawlowski, P. Szmeja, K. Wasielewska, and C. E. Palau, From implicit semantics towards ontologies—practical considerations from the INTER-IoT perspective, in Proc. of the 14th IEEE Annual Consumer Communications & Networking Conf., Las Vegas, NV, USA, 2017, pp. 59–64.
DOI
[54]
J. L. Cánovas Izquierdo and J. Cabot, Discovering implicit schemas in JSON data, in Proc. of the 13th Int. Conf. on Web Engineering, Aalborg, Denmark, 2013, pp. 68–83.
[55]
M. Klettke, U. Störl, and S. Scherzinger, Schema extraction and structural outlier detection for JSON-based NoSQL Data stores, in Proc. of the Conf. Database Systems for Business, Technology and Web, Hamburg, Germany, 2015, pp. 425–444.
[56]
D. S. Ruiz, S. F. Morales, and J. G. Molina, Inferring versioned schemas from NoSQL databases and its applications, in Proc. of the 34th Int. Conf. on Conceptual Modeling, Stockholm, Sweden, 2015, pp. 467–480.
[57]
L. J. Wang, S. Zhang, J. W. Shi, L. M. Jiao, O. Hassanzadeh, J. Zou, and C. Wang, Schema management for document stores, Proc. VLDB Endow., vol. 8, no. 9, pp. 922–933, 2015.
[58]
M. A. Baazizi, H. B. Lahmar, D. Colazzo, G. Ghelli, and C. Sartiani, Schema inference for massive JSON datasets, in Proc. 20th Int. Conf. on Extending Database Technology, Venice, Italy, 2017, pp. 222–233.
[59]
I. Comyn-Wattiau and J. Akoka, Model driven reverse engineering of NoSQL property graph databases: The case of Neo4j, in Proc. of 2017 IEEE Int. Conf. on Big Data, Boston, MA, USA, 2017, pp. 453–458.
[60]
W3C, Extensible markup language (XML) 1.0 (fifth edition) W3C recommendation 26 November 2008, , 2021.
[61]
M. Hacherouf, S. N. Bahloul, and C. Cruz, Transforming XML documents to OWL ontologies: A survey, J. Inf. Sci., vol. 41, no. 2, pp. 242–259, 2015.
[62]
W3C, XML schema part 0: Primer second edition W3C recommendation 28 October 2004, , 2021.
[63]
I. Bedini, C. Matheus, P. F. Patel-Schneider, A. Boran, and B. Nguyen, Transforming XML schema to OWL using patterns, in Proc. of the 2011 IEEE 5th Int. Conf. on Semantic Computing, Palo Alto, CA, USA, 2011, pp. 102–109.
[64]
M. Hacherouf and S. N. Bahloul, DTD2OWL2: A new approach for the transformation of the DTD to OWL, Procedia Comput. Sci., vol. 62, pp. 457–466, 2015.
[65]
A. Zekri, Z. Brahmia, F. Grandi, and R. Bouaziz, τOWL-Manager: A tool for managing temporal semantic web documents in the τOWL framework, in Proc. of the 9th Int. Conf. on Advances in Semantic Processing, Nice, France, 2015, pp. 56–64.
[66]
S. Jahangiri, Wisconsin benchmark data generator: To JSON and beyond, in Proc. 2021 Int. Conf. on Management of Data, Virtual Event, China, 2021, pp. 2887–2889.
[67]
R. Betík and I. Holubová, JBD generator: Towards semi-structured JSON big data, in Proc. of ADBIS 2016 Short Papers and Workshops, Prague, Czech Republic, 2016, pp. 54–62.
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Received: 27 August 2021
Revised: 27 October 2021
Accepted: 01 November 2021
Published: 18 July 2022
Issue date: December 2022

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© The author(s) 2022.

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